263-5255-00L Foundations of Reinforcement Learning
| Semester | Herbstsemester 2021 |
| Dozierende | N. He |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kommentar | Number of participants limited to 190. Last cancellation/deregistration date for this graded semester performance: Thursday, 28 October 2021! Please note that after that date no deregistration will be accepted and the course will be considered as "fail". |
Lehrveranstaltungen
| Nummer | Titel | Umfang | Dozierende | ||||
|---|---|---|---|---|---|---|---|
| 263-5255-00 V | Foundations of Reinforcement Learning | 2 Std. |
| N. He | |||
| 263-5255-00 A | Foundations of Reinforcement Learning | 2 Std. | N. He |
Katalogdaten
| Kurzbeschreibung | Reinforcement learning (RL) has been in the limelight of many recent breakthroughs in artificial intelligence. This course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets M.S. students with strong research interests in reinforcement learning, optimization, and control. |
| Lernziel | This course aims to provide students with an advanced introduction of RL theory and algorithms as well as bring them near the frontier of this active research field. By the end of the course, students will be able to - Identify the strengths and limitations of various reinforcement learning algorithms; - Formulate and solve sequential decision-making problems by applying relevant reinforcement learning tools; - Generalize or discover “new” applications, algorithms, or theories of reinforcement learning towards conducting independent research on the topic. |
| Inhalt | Basic topics include fundamentals of Markov decision processes, approximate dynamic programming, linear programming and primal-dual perspectives of RL, model-based and model-free RL, policy gradient and actor-critic algorithms, Markov games and multi-agent RL. If time allows, we will also discuss advanced topics such as batch RL, inverse RL, causal RL, etc. The course keeps strong emphasis on in-depth understanding of the mathematical modeling and theoretical properties of RL algorithms. |
| Skript | Lecture notes will be posted on Moodle. |
| Literatur | Dynamic Programming and Optimal Control, Vol I & II, Dimitris Bertsekas Reinforcement Learning: An Introduction, Second Edition, Richard Sutton and Andrew Barto. Algorithms for Reinforcement Learning, Csaba Czepesvári. Reinforcement Learning: Theory and Algorithms, Alekh Agarwal, Nan Jiang, Sham M. Kakade. |
| Voraussetzungen / Besonderes | Students are expected to have strong mathematical background in linear algebra, probability theory, optimization, and machine learning. |
Leistungskontrolle
| Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
| ECTS Kreditpunkte | 5 KP |
| Prüfende | N. He |
| Form | benotete Semesterleistung |
| Prüfungssprache | Englisch |
| Repetition | Repetition nur nach erneuter Belegung der Lerneinheit möglich. |
| Zusatzinformation zum Prüfungsmodus | project 60%, homework 40% |
Lernmaterialien
| Hauptlink | Information |
| Es werden nur die öffentlichen Lernmaterialien aufgeführt. | |
Gruppen
| Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
| Plätze | Maximal 190 |
| Vorrang | Die Belegung der Lerneinheit ist bis 04.10.2021 nur durch die primäre Zielgruppe möglich |
| Primäre Zielgruppe | Robotics, Systems and Control MSc (159000)
Elektrotechnik und Informationstechnologie MSc (237000) Doktorat Informationstechnologie & Elektrotechnik (239002) Cyber Security MSc (260000) Cyber Security MSc (EPFL) (260100) Data Science MSc (261000) Informatik MSc (263000) Doktorat Informatik (264002) CAS ETH in Informatik (269000) Statistik MSc (436000) |
| Warteliste | Bis 11.10.2021 |
